DeepMask’s usage analytics dashboard gives enterprise admins a clear view of how your team consumes AI across the platform. You can see total token usage, how tokens split between input and output, and a per-model breakdown showing which models your team relies on most. This data helps you manage AI spend, justify costs to stakeholders, and make informed decisions about which models to route different workloads to.Documentation Index
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What the usage dashboard shows
The dashboard surfaces three core metrics for your organization:Total tokens
The combined count of all tokens processed across every conversation and project in your workspace during the selected period.
Input tokens
Tokens sent to the model—your messages, uploaded file contents, system instructions, and project context. Input tokens typically represent the majority of consumption.
Output tokens
Tokens generated by the model in its responses. Output tokens are smaller in volume but often priced higher by underlying model providers.
Model breakdown
A per-model view showing how token consumption distributes across the 25+ models available in DeepMask—for example, Mistral Large, Opus 4.5, GPT-4, DeepSeek V3, and others your team has used.
Reading the model breakdown
The model breakdown is the most actionable part of the usage dashboard. It shows you which models consumed the most tokens during a given period, so you can answer questions like:- Are teams defaulting to the most expensive models for every task, including simple ones?
- Which departments or projects are the heaviest users of premium reasoning models?
- Has usage shifted since you onboarded a new team or started a new project?
High input token counts
High input token counts
A high input token count usually means your team is uploading large documents, using long system prompts, or working within projects with extensive context. Review project instructions and file uploads to ensure you’re only passing context that the model actually needs.
Concentrated usage on one model
Concentrated usage on one model
If the breakdown shows most tokens concentrated on a single high-cost model, consider whether all those tasks require that capability. Switching routine writing or summarization tasks to a faster model reduces cost without degrading output quality for those use cases.
Unexpected spikes in output tokens
Unexpected spikes in output tokens
Spikes in output tokens can indicate conversations asking for very long responses—full reports, detailed code, or extended analysis. Check whether those outputs are being used or whether response length can be constrained with project instructions.
Optimizing AI spend with usage data
Token usage data translates directly into cost. Here is how to use it to get better ROI from your DeepMask enterprise plan:Establish a baseline
Review your first month of usage to understand your team’s natural consumption patterns. Note which models dominate the breakdown and what total token volumes look like week over week.
Match models to task types
DeepMask gives you access to 25+ models. Use the breakdown to identify tasks running on premium reasoning models that could be handled by faster, lower-cost alternatives. Routine drafting, translation, and summarization rarely need extended thinking models.
Audit project context
Input tokens often grow as projects accumulate files and instructions. Periodically review project files and instructions to remove outdated context. Lean project context means lower input token counts on every conversation.
ROI context for enterprise teams
DeepMask is designed so that AI scales with your company, not just individuals. Usage analytics give you the organizational visibility to measure the return on your AI investment—not just whether people are using it, but how, and at what cost.